A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds

• URL: http://arxiv.org/abs/2005.02249v1
• Date: Tue, 5 May 2020 14:47:35 GMT
• Title: A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
• Authors: Maxim S. Kovalev and Lev V. Utkin
• Abstract summary: SurvLIME-KS is proposed for explaining machine learning survival models. It is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.
• Score: 5.482532589225552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A new robust algorithm based of the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov-Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov-Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency.

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